Prediction models are applied to predict future trends or behavior patterns (e.g., a risk of default) from given input data. Typically, a prediction model is trained with transaction data associated with entities for achieving better operational efficiencies, reducing fraudulent transactions and the like. Often, the amount of transactional data is significantly large (e.g., in terabytes and gigabytes) and changes frequently (e.g., daily). Therefore, the process of training the prediction model could be time consuming, and it may be difficult to regularly check the accuracy of the prediction model. Further, training the prediction model periodically (e.g., monthly) may not be accurate since the transaction data used to train the prediction model may become old (e.g., by a month) and relying on old transaction data might be risky.